Killer Applications Coming to AI Soon

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MARKET-SAVING DEMAND

Killer Applications Coming to AI

With markets showing serious signs that investors are concerned (with any minor inconvenient causing a massive sell-off of big tech stocks), AI faces a real challenge:

How do we justify the hype? And the answer… is killer applications.

For that reason, today, we are looking at what research and numbers point to as the first-to-come super applications of AI, a list packed with surprises, including what I consider the first real job in which AI will retire humanity and winners and losers in each category.

Let’s dive in.

Current Technological Limitations

To justify which killer applications will come first, we need to understand the current limitations that have prevented their emergence and the technologies that will soon allow them to finally emerge.

Fighting hallucinations

We first have the most common problem: when the model confabulates reality or facts, a term commonly known as ‘hallucination.’

The term ‘hallucination’ is wrong. Confabulate is more accurate. For a detailed description of this, read my Medium blog article.

Worse off, the model is tremendously eloquent, almost arrogant, increasing the chances the user gets deceived. But here’s the thing: this is all they do.

On first principles, every single prediction is a ‘hallucination.’ But why?

Although everyone defines their inner workings as ‘predicting the next word to a sequence,’ that is, in fact, a poor way of describing how they work.

In reality, they predict the probability distribution over the next word, a fancy way of saying that the model ranks all the words it knows according to how adequate they are as continuations to the sequence.

But here’s where things get tricky. They don’t always pick the most likely one (‘Playground’ in the image above), but they randomly choose one of the most probable.

While this enhances the model’s creativity, it increases the likelihood of choosing a word that distorts the truth. In other words, they don’t confabulate only when they get things wrong; it’s all they do.

Needless to say, hallucinations are the biggest issue with Large Language Models (LLMs) today. But why do I think this is about to change?

Augmented LLMs.

Although I covered them extensively in a previous newsletter issue, the idea is that while we can retain the model’s creativity (aka saying the same thing in various ways, which leads to more frequent hallucinations), for specific instances like facts, we can use memory adapters to collapse the distribution into the exact word.

But what does that mean?

Using the example below, a standard LLM (middle) would consider ‘1981’ and ‘1970’ as semantically probable next continuations. However, while both are semantically valid, only one is true.

But as I was saying,…

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